We train two baseline models once inputting all the features including the sensitive attribute and once removing this attribute. We assume that simply removing the sensitive attribute does not help to omit the gender bias [1]}. The performances are compared with our fair model and if the sensitive attribute is added back, see Fig. . For each model we retrain the last layer (hidden layers are fixed) to predict once the gender of the input samples and once the income. Results are summarized in Tab. REF . The accuracies of the baseline models lay very close together, which shows that information about the gender attribute is indeed still hidden in the input data. Our model is not able to classify the genders correctly just labeling almost all samples as male. The performance of our model with reattached gender attribute is similar to the baseline models. This shows that the sensitive attribute helps the model to perform better.
